Vetted XGBoost Professionals

“Data/ML engineer from HCLTech who modernized enterprise data by linking fragmented financial and supply-chain data across SAP/SQL Server/Snowflake using NLP entity linking and embeddings (FAISS). Delivered measurable impact including ~40% reduction in manual error-log triage and entity-linking accuracy improvements from ~86% to ~93%, with results surfaced in Power BI for real-time analytics.”

Arjun Shrestha

Screened

Intern AI/GenAI Engineer specializing in NLP, RAG, and Snowflake Cortex

Columbus, Ohio1y exp

“Built and deployed a production AI invention/patent review platform that compares invention submissions against patent rules to provide instant feedback, reportedly cutting legal team review time by ~80%. Learned Snowflake Cortex LLMs and production deployment (Docker + AWS) on the job, and validated system quality through human-in-the-loop testing with experienced legal stakeholders.”

Kundhana Paruchuru

Screened

Mid-level Data Scientist specializing in ML, LLM pipelines, and MLOps

Remote, USA3y exp

“Built and deployed a production LLM-driven document understanding pipeline using LangChain/LangGraph, focusing on reliability via step-by-step prompting, validation checks, and monitoring. Also partnered with non-technical marketing stakeholders at Heartland Community Network to deliver an XGBoost targeting model surfaced in Power BI, improving campaign conversion by 12%.”

Yash Amre

Screened

Intern Data Scientist specializing in machine learning and NLP

California, USA1y exp

“Analytics-focused early-career candidate with internship experience owning reporting and system performance analysis projects end to end. They combine SQL data preparation, Python automation, and dashboard delivery with measurable impact, including roughly 50% less manual reporting and about 20% better forecast accuracy.”

Keeravani Chekuri

Screened

Mid-level AI/ML Engineer specializing in LLM systems and MLOps

Boston, MA3y exp

“Built and deployed an AI tutoring assistant end-to-end at Nexora School, spanning discovery with school districts, multi-agent LangGraph/RAG architecture, AWS Bedrock migration, and post-launch stabilization. Stands out for combining hands-on LLM systems engineering with strong educator-facing trust building, FERPA-driven architecture decisions, and disciplined production practices around evals, logging, and messy document ingestion.”

“At Provenbase, built and shipped a production LLM-powered semantic search and candidate matching platform (RAG with GPT-4/Gemini, multi-agent orchestration, Elasticsearch vector search) to scale sourcing across 10M+ candidate records and 1000+ data sources. Drove sub-second performance, cut LLM spend 30% with routing/caching, and improved recruiting outcomes (+45% sourcing accuracy; +38% visibility of underrepresented talent) through bias-aware ranking and tight collaboration with recruiting stakeholders.”

Meghana Chowdary Borra

Screened

Junior Machine Learning Engineer specializing in predictive modeling and GenAI RAG systems

Buffalo, New York2y exp

“LLM engineer who built and deployed an emotionally intelligent AAC communication system using an emotion-aware RAG pipeline (Empathetic Dialogues + GoEmotions) and a PEFT-adapted model. Experienced with LangChain/LangGraph and custom Python orchestration, focusing on reliability (guards, schema validation, fallbacks), latency optimization, and rigorous evaluation (automatic metrics + human-in-the-loop), with a reported 18% user satisfaction improvement.”

Lakshmi Meghana

Screened

Mid-level AI/ML Engineer specializing in production ML, MLOps, and NLP

Bristol, PA4y exp

“Built and deployed a transformer-based clinical document classification system that processes unstructured clinical notes in a HIPAA-compliant healthcare setting, served via FastAPI on AWS and integrated into an Airflow/S3 pipeline. Demonstrates strong end-to-end MLOps skills (data quality remediation, low-latency inference optimization, monitoring with MLflow/CloudWatch) and effective collaboration with clinicians to drive adoption.”

Krishna K

Screened

Junior Machine Learning Engineer specializing in multimodal systems and LLMs

Jersey City, NJ2y exp

“Built and productionized a domain-specific LLM-powered RAG knowledge assistant at JerseyStem for answering questions over large internal document corpora, owning the full stack from FAISS retrieval and LoRA/QLoRA fine-tuning to AWS autoscaling GPU deployment. Drove measurable gains (28% accuracy lift, 25% latency reduction) and improved reliability through hybrid retrieval, grounded decoding, preference-model reranking, and Airflow-orchestrated pipelines (35% faster runtime), while partnering closely with non-technical stakeholders to define success metrics and ensure adoption.”

Aneri Patel

Screened

Junior Machine Learning Engineer specializing in LLM fine-tuning and semantic retrieval

Washington, D.C.2y exp

“Backend engineer with legal-tech and AI workflow experience: built JurisAI, an end-to-end legal research system using OCR + embeddings + Pinecone vector search to deliver citation-grounded LLM answers with safe failure modes (~90% recall@K). Also led a GW Law metadata migration into Caspio with batch validation and parallel rollout, and has strong FastAPI/GCP production reliability and observability practices.”

Mohammed Syed

Screened

Mid-level AI Engineer & Researcher specializing in healthcare AI and multimodal LLM systems

Remote2y exp

“Backend/ML engineer focused on clinical AI transparency who built ShifaMind, an explainability-enforced clinical ML system using UMLS/MIMIC-IV/PubMed data with RAG, GraphSAGE, and cross-attention. Demonstrated strong production engineering via FastAPI API design and safe migrations (feature flags/shadow inference), plus HIPAA-aligned auth/RLS patterns; also delivered a real-time comet detection system reaching 97.7% accuracy.”

RAUNAQ BEDI

Screened

Entry-level Software Engineer specializing in AI, data engineering, and cloud DevOps

San Francisco, CA1y exp

“Product-minded full-stack engineer with strong React/TypeScript, serverless AWS, and Postgres depth, highlighted by owning real-time personalization and onboarding experiences at mParticle. Stands out for combining deep performance debugging with measurable product impact—improving activation by 28%, reducing time-to-insights by 35%, and building reusable internal platform primitives adopted by 12 teams.”

vinod yendluri

Screened

Mid-level MLOps Engineer specializing in production machine learning systems

Hyderabad, India3y exp

“Built an end-to-end churn prediction platform at Freddi's Flowers spanning Spark ETL on AWS, model serving, monitoring, and a stakeholder-facing dashboard. Stands out for combining MLOps rigor with product thinking—adding explainability, action-oriented workflows, and config-driven multi-tenant architecture while improving latency and automating drift response.”